What's in a Name? The Effects of the Labels “Fat” Versus “Overweight” on Weight Bias1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research examined the effects of the labels “fat” vs. “overweight” in the expression of weight bias, with the prediction that the label “fat” biases individuals to respond more negatively than does the label “overweight.” In Study 1, participants' attitudes toward people labeled as fat were less favorable than were their attitudes toward people labeled as overweight. In Studies 2 and 3, although participants chose similar-sized figures to depict fat and overweight targets, weight stereotypes and weight attitudes were more negative toward people labeled as fat than those labeled as overweight. In addition, the endorsement of weight stereotypes mediated the biasing effect of the “fat” label on weight prejudice. Implications of this work for prejudice researchers and for public attitudes are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it